Book Image

Intelligent Workloads at the Edge

By : Indraneel Mitra, Ryan Burke
Book Image

Intelligent Workloads at the Edge

By: Indraneel Mitra, Ryan Burke

Overview of this book

The Internet of Things (IoT) has transformed how people think about and interact with the world. The ubiquitous deployment of sensors around us makes it possible to study the world at any level of accuracy and enable data-driven decision-making anywhere. Data analytics and machine learning (ML) powered by elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated by IoT. Now, edge computing has brought information technologies closer to the data source to lower latency and reduce costs. This book will teach you how to combine the technologies of edge computing, data analytics, and ML to deliver next-generation cyber-physical outcomes. You’ll begin by discovering how to create software applications that run on edge devices with AWS IoT Greengrass. As you advance, you’ll learn how to process and stream IoT data from the edge to the cloud and use it to train ML models using Amazon SageMaker. The book also shows you how to train these models and run them at the edge for optimized performance, cost savings, and data compliance. By the end of this IoT book, you’ll be able to scope your own IoT workloads, bring the power of ML to the edge, and operate those workloads in a production setting.
Table of Contents (17 chapters)
1
Section 1: Introduction and Prerequisites
3
Section 2: Building Blocks
10
Section 3: Scaling It Up
13
Section 4: Bring It All Together

Onboarding a fleet of devices globally

We already introduced you to the different activities involved in the IoT manufacturing supply chain in Chapter 8, DevOps and MLOps for the Edge. Onboarding refers to the process of manufacturing, assembling, and registering a device with a registration authority. In this section, we will dive deeper into the following activities that play a part in the onboarding workflow:

Figure 9.1 – Device onboarding activities

So far, in this book, you have been using a Raspberry Pi (or a virtual environment) to perform the hands-on exercises. This is a common practice for development and prototyping needs. However, as your project progresses toward a higher environment (such as QA or production), it is recommended that you consider hardware that's industry-grade and can operate in various conditions. Therefore, all the aforementioned activities in Figure 9.1 need to be completed before your device (that is, the connected...